Building Interdisciplinary Capacity for Understanding and Supporting Computer Supported Collaborative Learning

建立理解和支持计算机支持的协作学习的跨学科能力数据科学技术使许多学术领域发生了革命性的变化，并在商业领域带来了巨大的收益。到目前为止，它们在解决美国教育系统中的关键问题方面一直没有得到充分利用，特别是在了解科学、技术、工程和数学(STEM)学习和学习环境、扩大STEM的参与以及增加传统上STEM服务不足的学生的留住方面。教育和人力资源局通过推进大数据科学与工程(BigData)计划的关键技术和技术(BigData)计划的目标是促进对该领域关键问题的基本理解，并促进数据科学在教育研究中的应用。合作有着悠久的研究历史和坚实的研究基础。然而，由于该领域使用的标准研究方法，复制或汇总结果的难度阻碍了这项工作。目前合作研究的标准做法是记录学生在音频或视频上的活动，创建定性编码方案，并手动编码数据。这是劳动密集型的，容易受到个人判断和错误的影响。这个项目将调查数据挖掘和学习分析的使用，以克服这一聚合和准确性的挑战。该提案的主要目标是举办一系列研讨会，通过介绍和调查学习分析和数据挖掘技术的最佳使用，促进研究人员社区调查协作的效果。

Building Interdisciplinary Capacity for Understanding and Supporting Computer Supported Collaborative LearningData science techniques have revolutionized many academic fields and led to terrific gains in the commercial sector. They have to date been underutilized in solving critical problems in the US educational system, particularly in understanding Science, Technology, Engineering and Mathematics (STEM) learning and learning environments, broadening participation in STEM, and increasing retention for students traditionally underserved in STEM. The goals of the Directorate for Education and Human Resources through the Critical Techniques and Technologies for Advancing Foundations and Applications of Big Data Science & Engineering (BIGDATA) program are to advance fundamental understanding of key questions in the field, and catalyze the use of data science in Education Research. Collaboration has a long history of research and a solid research base. However, the work is hampered by the difficulty of replicating or aggregating results due to the standard research methods used in the field. The current standard practice in collaboration research is to record student activity on audio or video, create qualitative coding schemes, and code the data by hand. This is labor intensive and prone to personal judgment and error. This project will investigate the use of data mining and learning analytics to overcome this challenge of aggregation and accuracy.The main goal of the proposal is to conduct a series of workshops to catalyze the community of researchers investigating the effects of collaboration by introducing and investigating the best uses of learning analytics and data mining techniques.